{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Structual Decomposition Analysis\n",
    "\n",
    "Author: <a href=\"mailto:a.owen@leeds.ac.uk\"> Dr Anne Owen </a> \n",
    "\n",
    "Before we start, paste the following code into the box below:\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import copy\n",
    "import matplotlib\n",
    "pd.options.display.precision = 2\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import copy\n",
    "import matplotlib\n",
    "pd.options.display.precision = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we will continue to use The World Input Output Database (WIOD) but we will use the datatables in constant 2001 prices. \n",
    "\n",
    "\n",
    "WIOD contains 30 countries and the Rest of the World, but this week we are going to focus solely on the UK for the period 2001-2014. \n",
    "\n",
    "Recall that data for 56 sectors are classified according to the International Standard Industrial Classification revision 3 (ISIC Rev. 3).\n",
    "\n",
    "We are particularly interested in the change in footprint from 2001 and will initially need a function that simply calculates the total footprint.\n",
    "\n",
    "## Exercise 1.1 Make total footprint function\n",
    "\n",
    "Copy this function into the box below:\n",
    "\n",
    "```python\n",
    "def total_footprint_calc(Z,Y,y_region,f):   \n",
    "    x = np.sum(Z,1) + np.sum(Y,1)\n",
    "    x[x==0] = 0.000000001\n",
    "    big_X = np.tile(np.transpose(x),[30*56,1])\n",
    "    A = Z/big_X \n",
    "    L = np.linalg.inv(np.identity(30*56)-A)\n",
    "    e = f/x \n",
    "    eL = np.dot(e,L)\n",
    "    footprint = np.dot(eL,y_region)\n",
    "\n",
    "    return footprint\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def total_footprint_calc(Z,Y,y_region,f):   \n",
    "    x = np.sum(Z,1) + np.sum(Y,1)\n",
    "    x[x==0] = 0.000000001\n",
    "    big_X = np.tile(np.transpose(x),[30*56,1])\n",
    "    A = Z/big_X \n",
    "    L = np.linalg.inv(np.identity(30*56)-A)\n",
    "    e = f/x \n",
    "    eL = np.dot(e,L)\n",
    "    footprint = np.sum(np.sum(np.dot(eL,y_region),0),0)\n",
    "\n",
    "    return footprint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1.2: Load the data\n",
    "\n",
    "Before we can load the data we must set up some empty dictionaries. Write:\n",
    "\n",
    "```python\n",
    "Z = {}\n",
    "Y = {}\n",
    "f = {}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = {}\n",
    "Y = {}\n",
    "f = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each individual year of data for each of ```Z```, ```Y``` and ```f``` is contained in a csv file.\n",
    "\n",
    "The country name and sector names are found in the first column and row of the ```Z``` files.\n",
    "\n",
    "The country names are found in the first row of the ```Y``` files and the country name and sector names are found in the first column of the ```Y``` files.\n",
    "\n",
    "The country name and sector names are found in the first row of the ```f``` files.\n",
    "\n",
    "Use following code, similar to the last set of exercises:\n",
    "\n",
    "```python\n",
    "for yr in range (2000,2015):\n",
    "    print(yr)\n",
    "    Z[yr] = pd.read_csv('WIODv2016_data_deflated/z_' +str(yr)+ '_2000prices.csv', header=0,index_col=0)\n",
    "    Y[yr] = pd.read_csv('WIODv2016_data_deflated/y_' +str(yr)+ '_2000prices.csv', header=0,index_col=0)\n",
    "    f[yr] = np.transpose(pd.read_csv('WIODv2016_data_deflated/f_' +str(yr)+ '.csv', header=0,index_col=0))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n",
      "2001\n",
      "2002\n",
      "2003\n",
      "2004\n",
      "2005\n",
      "2006\n",
      "2007\n",
      "2008\n",
      "2009\n",
      "2010\n",
      "2011\n",
      "2012\n",
      "2013\n",
      "2014\n"
     ]
    }
   ],
   "source": [
    "for yr in range (2000,2015):\n",
    "    print(yr)\n",
    "    Z[yr] = pd.read_csv('WIODv2016_data_deflated/z_' +str(yr)+ '_2000prices.csv', header=0,index_col=0)\n",
    "    Y[yr] = pd.read_csv('WIODv2016_data_deflated/y_' +str(yr)+ '_2000prices.csv', header=0,index_col=0)\n",
    "    f[yr] = np.transpose(pd.read_csv('WIODv2016_data_deflated/f_' +str(yr)+ '.csv', header=0,index_col=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1.3 Using a for-loop to find the UK footprint for the years 2000 to 2014\n",
    "\n",
    "First let's create a varaible years that contains the range 2000-2014\n",
    "\n",
    "```python\n",
    "    years = range(2000,2015)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = range(2000,2015)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will set up a new empty variable called data which will have one column for  the carbon footprint for the years 2000 to 2015 and a second column which will show change over time from 2000. \n",
    "\n",
    "Try:\n",
    "\n",
    "```python\n",
    "    data = np.zeros((15,2))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.zeros((15,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to set up a for-loop to call the footprint function for every year in ```years```. \n",
    "\n",
    "We need to set the ```y_region``` variable to use the column for the UK. We do this using ```Y.loc[:,'GBR']```. Selecting the column from Y which has 'GBR' as the column heading.\n",
    "\n",
    "We will put the result into our data variable. The problem is that we will need to tell Python which cell to put the result in. This is where the enumerate function becomes really useful. Enumerate will count how many times we have looped through the function and we can use it to decide which cell to fill.\n",
    "\n",
    "Finally, we calculate the change from 1995 by looking up the current year's footprint ```data[count,0]``` and subtracting the footprint recorded for 1995 found in the first row, ```data[0,0]```\n",
    "\n",
    "Try:\n",
    "\n",
    "```python\n",
    "for count, yr in enumerate(years):\n",
    "    print(yr)\n",
    "    data[count,0] = total_footprint_calc(Z[yr],Y[yr],Y[yr].loc[:,'GBR'],f[yr])\n",
    "    data[count,1] = data[count,0]-data[0,0]\n",
    "        \n",
    "UK_foot = pd.DataFrame(data,index=years,columns=['footprint','change from 2000'])\n",
    "UK_foot\n",
    "```\n",
    "\n",
    "The last two lines of code make and display a nice dataframe with headings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n",
      "2001\n",
      "2002\n",
      "2003\n",
      "2004\n",
      "2005\n",
      "2006\n",
      "2007\n",
      "2008\n",
      "2009\n",
      "2010\n",
      "2011\n",
      "2012\n",
      "2013\n",
      "2014\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>footprint</th>\n",
       "      <th>change from 2000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>604080.93</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>622181.67</td>\n",
       "      <td>18100.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>634619.66</td>\n",
       "      <td>30538.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>662956.02</td>\n",
       "      <td>58875.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>684299.47</td>\n",
       "      <td>80218.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>693644.20</td>\n",
       "      <td>89563.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>702782.52</td>\n",
       "      <td>98701.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>701719.29</td>\n",
       "      <td>97638.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>692898.42</td>\n",
       "      <td>88817.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>603926.22</td>\n",
       "      <td>-154.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>604721.10</td>\n",
       "      <td>640.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>587078.61</td>\n",
       "      <td>-17002.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>637197.53</td>\n",
       "      <td>33116.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>605114.23</td>\n",
       "      <td>1033.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>569615.13</td>\n",
       "      <td>-34465.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      footprint  change from 2000\n",
       "2000  604080.93              0.00\n",
       "2001  622181.67          18100.74\n",
       "2002  634619.66          30538.74\n",
       "2003  662956.02          58875.09\n",
       "2004  684299.47          80218.54\n",
       "2005  693644.20          89563.28\n",
       "2006  702782.52          98701.59\n",
       "2007  701719.29          97638.36\n",
       "2008  692898.42          88817.49\n",
       "2009  603926.22           -154.70\n",
       "2010  604721.10            640.17\n",
       "2011  587078.61         -17002.31\n",
       "2012  637197.53          33116.60\n",
       "2013  605114.23           1033.30\n",
       "2014  569615.13         -34465.79"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for count, yr in enumerate(years):\n",
    "    print(yr)\n",
    "    data[count,0] = total_footprint_calc(Z[yr],Y[yr],Y[yr].loc[:,'GBR'],f[yr])\n",
    "    data[count,1] = data[count,0]-data[0,0]\n",
    "\n",
    "UK_foot = pd.DataFrame(data,index=years,columns=['footprint','change from 2000'])\n",
    "UK_foot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets make a graph of change in footprint from 2001.\n",
    "\n",
    "Try\n",
    "```python\n",
    "change = UK_foot.iloc[:,1]\n",
    "chart = change.plot( kind = 'line')\n",
    "chart.set_ylabel('change from 2000 (kilotonnes CO2)')\n",
    "```\n",
    "\n",
    "The numbers are ever-so slightly different from those calculated last week because we are using the constant price database and the allocations of global emissions shift a tiny bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'change from 2000 (kilotonnes CO2)')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "change = UK_foot.iloc[:,1]\n",
    "chart = change.plot( kind = 'line')\n",
    "chart.set_ylabel('change from 2000 (kilotonnes CO2)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2.1 Checking the Structural Decomposition Equation\n",
    "\n",
    "For the decomposition work, because we want our decomposition to align with IPAT/Kaya principles, we have rewritten the footprint equation from:\n",
    "\n",
    "$$\n",
    "\\mathbf{Q = eLy}\n",
    "$$\n",
    "\n",
    "to \n",
    "\n",
    "$$\n",
    "\\mathbf{Q=isp}\n",
    "$$\n",
    "\n",
    "where $\\mathbf{i = eL}$ and $\\mathbf{s =} \\frac{y}{p}$\n",
    "\n",
    "Before we start calculating decompositions, let's check that this new equation works.\n",
    "\n",
    "First you will need a vector of UK population by year.\n",
    "\n",
    "```python\n",
    "pop_data = [58893000,59120000,59370000,59648000,59999000,60401000,60847000,61322000,61807000,62276000,62766000,63259000,63700000,64128000,64602000]\n",
    "\n",
    "p = pd.DataFrame(pop_data,index=years)\n",
    "display(p)\n",
    "```\n",
    "\n",
    "These population figures have been taken from the World Bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>58893000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>59120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>59370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>59648000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>59999000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>60401000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>60847000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>61322000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>61807000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>62276000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>62766000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>63259000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>63700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>64128000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>64602000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "2000  58893000\n",
       "2001  59120000\n",
       "2002  59370000\n",
       "2003  59648000\n",
       "2004  59999000\n",
       "2005  60401000\n",
       "2006  60847000\n",
       "2007  61322000\n",
       "2008  61807000\n",
       "2009  62276000\n",
       "2010  62766000\n",
       "2011  63259000\n",
       "2012  63700000\n",
       "2013  64128000\n",
       "2014  64602000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_data = [58893000,59120000,59370000,59648000,59999000,60401000,60847000,61322000,61807000,62276000,62766000,63259000,63700000,64128000,64602000]\n",
    "\n",
    "p = pd.DataFrame(pop_data,index=years)\n",
    "display(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now you need to make $\\mathbf{i=eL}$\n",
    "\n",
    "We can take code from the footprint function at the start of this workbook.\n",
    "\n",
    "First we set up a data dictionary for i using ```i={}```.\n",
    "\n",
    "The final part of the function places the new dataframe ```i_data```, which is made multiplying ```e``` by ```L```, into the dictionary for each year.\n",
    "\n",
    "```python\n",
    "i = {}\n",
    "for yr in years:\n",
    "    x = np.sum(Z[yr],1) + np.sum(Y[yr],1)\n",
    "    x[x==0] = 0.000000001\n",
    "    big_X = np.tile(np.transpose(x),[30*56,1])\n",
    "    A = Z[yr]/big_X \n",
    "    L = np.linalg.inv(np.identity(30*56)-A)\n",
    "    e = f[yr]/x \n",
    "    i_data = pd.DataFrame(np.dot(e,L),columns=Y[2000].index)\n",
    "    i[yr] = i_data\n",
    "    \n",
    "display(i[2000])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n",
      "2001\n",
      "2002\n",
      "2003\n",
      "2004\n",
      "2005\n",
      "2006\n",
      "2007\n",
      "2008\n",
      "2009\n",
      "2010\n",
      "2011\n",
      "2012\n",
      "2013\n",
      "2014\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUT Crop and animal production, hunting and related service activities</th>\n",
       "      <th>AUT Forestry and logging</th>\n",
       "      <th>AUT Fishing and aquaculture</th>\n",
       "      <th>AUT Mining and quarrying</th>\n",
       "      <th>AUT Manufacture of food products, beverages and tobacco products</th>\n",
       "      <th>AUT Manufacture of textiles, wearing apparel and leather products</th>\n",
       "      <th>AUT Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials</th>\n",
       "      <th>AUT Manufacture of paper and paper products</th>\n",
       "      <th>AUT Printing and reproduction of recorded media</th>\n",
       "      <th>AUT Manufacture of coke and refined petroleum products</th>\n",
       "      <th>...</th>\n",
       "      <th>RoW Scientific research and development</th>\n",
       "      <th>RoW Advertising and market research</th>\n",
       "      <th>RoW Other professional, scientific and technical activities; veterinary activities</th>\n",
       "      <th>RoW Administrative and support service activities</th>\n",
       "      <th>RoW Public administration and defence; compulsory social security</th>\n",
       "      <th>RoW Education</th>\n",
       "      <th>RoW Human health and social work activities</th>\n",
       "      <th>RoW Other service activities</th>\n",
       "      <th>RoW Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use</th>\n",
       "      <th>RoW Activities of extraterritorial organizations and bodies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>3.50e-05</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 1680 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AUT Crop and animal production, hunting and related service activities  \\\n",
       "0                                               0.28                        \n",
       "\n",
       "   AUT Forestry and logging  AUT Fishing and aquaculture  \\\n",
       "0                      0.32                          0.2   \n",
       "\n",
       "   AUT Mining and quarrying  \\\n",
       "0                      0.24   \n",
       "\n",
       "   AUT Manufacture of food products, beverages and tobacco products  \\\n",
       "0                                               0.31                  \n",
       "\n",
       "   AUT Manufacture of textiles, wearing apparel and leather products  \\\n",
       "0                                               0.48                   \n",
       "\n",
       "   AUT Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials  \\\n",
       "0                                                0.4                                                                                     \n",
       "\n",
       "   AUT Manufacture of paper and paper products  \\\n",
       "0                                         0.79   \n",
       "\n",
       "   AUT Printing and reproduction of recorded media  \\\n",
       "0                                             0.37   \n",
       "\n",
       "   AUT Manufacture of coke and refined petroleum products   ...  \\\n",
       "0                                               0.53        ...   \n",
       "\n",
       "   RoW Scientific research and development  \\\n",
       "0                                     0.81   \n",
       "\n",
       "   RoW Advertising and market research  \\\n",
       "0                                  0.0   \n",
       "\n",
       "   RoW Other professional, scientific and technical activities; veterinary activities  \\\n",
       "0                                               0.82                                    \n",
       "\n",
       "   RoW Administrative and support service activities  \\\n",
       "0                                               0.46   \n",
       "\n",
       "   RoW Public administration and defence; compulsory social security  \\\n",
       "0                                               0.96                   \n",
       "\n",
       "   RoW Education  RoW Human health and social work activities  \\\n",
       "0           0.77                                         1.07   \n",
       "\n",
       "   RoW Other service activities  \\\n",
       "0                          1.08   \n",
       "\n",
       "   RoW Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use  \\\n",
       "0                                           3.50e-05                                                                                \n",
       "\n",
       "   RoW Activities of extraterritorial organizations and bodies  \n",
       "0                                               1.39            \n",
       "\n",
       "[1 rows x 1680 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = {}\n",
    "for yr in years:\n",
    "    print(yr)\n",
    "    x = np.sum(Z[yr],1) + np.sum(Y[yr],1)\n",
    "    x[x==0] = 0.000000001\n",
    "    big_X = np.tile(np.transpose(x),[30*56,1])\n",
    "    A = Z[yr]/big_X \n",
    "    L = np.linalg.inv(np.identity(30*56)-A)\n",
    "    e = f[yr]/x \n",
    "    i_data = pd.DataFrame(np.dot(e,L),columns=Y[2000].index)\n",
    "    i[yr] = i_data\n",
    "\n",
    "display(i[2000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember, $\\mathbf{i=eL}$, is a row vector showing full supply chain product emissions per $ spend\n",
    "\n",
    "now we make $\\mathbf{s=\\frac{y}{p}}$. This is spend on products per capita.\n",
    "\n",
    "Try\n",
    "\n",
    "```python\n",
    "s = {}\n",
    "for yr in years:\n",
    "    s_data = pd.DataFrame(Y[yr].loc[:,'GBR'].values/p.loc[yr].values,index = Y[2000].index)\n",
    "    s[yr] = s_data\n",
    "display(s[2000])\n",
    "```\n",
    "\n",
    "This is quite straightforward. The only complication here is getting the correct values from the population dataframe ```p```. Remember to select values from a dataframe that correspond to a row heading, we use ```loc```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUT Crop and animal production, hunting and related service activities</th>\n",
       "      <td>4.16e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUT Forestry and logging</th>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUT Fishing and aquaculture</th>\n",
       "      <td>2.89e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUT Mining and quarrying</th>\n",
       "      <td>3.02e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUT Manufacture of food products, beverages and tobacco products</th>\n",
       "      <td>1.11e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoW Education</th>\n",
       "      <td>4.68e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoW Human health and social work activities</th>\n",
       "      <td>1.27e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoW Other service activities</th>\n",
       "      <td>2.05e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoW Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use</th>\n",
       "      <td>3.63e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoW Activities of extraterritorial organizations and bodies</th>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1680 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           0\n",
       "AUT Crop and animal production, hunting and rel...  4.16e-07\n",
       "AUT Forestry and logging                            0.00e+00\n",
       "AUT Fishing and aquaculture                         2.89e-08\n",
       "AUT Mining and quarrying                            3.02e-07\n",
       "AUT Manufacture of food products, beverages and...  1.11e-05\n",
       "...                                                      ...\n",
       "RoW Education                                       4.68e-06\n",
       "RoW Human health and social work activities         1.27e-05\n",
       "RoW Other service activities                        2.05e-05\n",
       "RoW Activities of households as employers; undi...  3.63e-07\n",
       "RoW Activities of extraterritorial organization...  0.00e+00\n",
       "\n",
       "[1680 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = {}\n",
    "for yr in years:\n",
    "    s_data = pd.DataFrame(Y[yr].loc[:,'GBR'].values/p.loc[yr].values,index = Y[2000].index)\n",
    "    s[yr] = s_data\n",
    "display(s[2000])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember, $\\mathbf{s=\\frac{y}{p}}$, is a column vector showing spend per product per capita. The units in this case are $million per person.\n",
    "\n",
    "\n",
    "We are now going to multiply everything together: $\\mathbf{Q=isp}$\n",
    "\n",
    "Because ```i``` and ```s``` are vectors, we will be using matrix functions. A row vector multiplied by a column vector should give a single value. Population is a scalar (a single number) so is simply multipled at the end. \n",
    "\n",
    "We'll put our new data into a DataFrame ```UK_foot2``` and check that it is the same as ```UK_foot```, calculated earlier.\n",
    "\n",
    "```python\n",
    "check_data = np.zeros((15,2))\n",
    "\n",
    "for count, yr in enumerate(years):\n",
    "    check_data[count,0] = np.dot(i[yr],s[yr])*p.loc[yr].values\n",
    "    check_data[count,1] = check_data[count,0]-check_data[0,0]\n",
    "\n",
    "\n",
    "UK_foot2 = pd.DataFrame(check_data,index=years,columns=['footprint','change from 1995'])\n",
    "UK_foot2\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>footprint</th>\n",
       "      <th>change from 2000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>604080.93</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>622181.67</td>\n",
       "      <td>18100.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>634619.66</td>\n",
       "      <td>30538.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>662956.02</td>\n",
       "      <td>58875.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>684299.47</td>\n",
       "      <td>80218.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>693644.20</td>\n",
       "      <td>89563.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>702782.52</td>\n",
       "      <td>98701.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>701719.29</td>\n",
       "      <td>97638.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>692898.42</td>\n",
       "      <td>88817.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>603926.22</td>\n",
       "      <td>-154.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>604721.10</td>\n",
       "      <td>640.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>587078.61</td>\n",
       "      <td>-17002.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>637197.53</td>\n",
       "      <td>33116.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>605114.23</td>\n",
       "      <td>1033.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>569615.13</td>\n",
       "      <td>-34465.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      footprint  change from 2000\n",
       "2000  604080.93              0.00\n",
       "2001  622181.67          18100.74\n",
       "2002  634619.66          30538.74\n",
       "2003  662956.02          58875.09\n",
       "2004  684299.47          80218.54\n",
       "2005  693644.20          89563.28\n",
       "2006  702782.52          98701.59\n",
       "2007  701719.29          97638.36\n",
       "2008  692898.42          88817.49\n",
       "2009  603926.22           -154.70\n",
       "2010  604721.10            640.17\n",
       "2011  587078.61         -17002.31\n",
       "2012  637197.53          33116.60\n",
       "2013  605114.23           1033.30\n",
       "2014  569615.13         -34465.79"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_data = np.zeros((15,2))\n",
    "\n",
    "for count, yr in enumerate(years):\n",
    "    check_data[count,0] = np.sum(np.sum(np.dot(i[yr],s[yr])*p.loc[yr].values,0),0)\n",
    "    check_data[count,1] = check_data[count,0]-check_data[0,0]\n",
    "\n",
    "\n",
    "UK_foot2 = pd.DataFrame(check_data,index=years,columns=['footprint','change from 2000'])\n",
    "UK_foot2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Has it worked? \n",
    "\n",
    "Have we successfully calculated the footprint using variables that resemble IPAT/Kaya?\n",
    "\n",
    "Now let's use Structural Decomposition Analysis to find out the effect of change in product emissions intensities, changes in spend per person and population change on the change in the UK's footprint!\n",
    "\n",
    "## Exercise 2.2 Making the Structural Decomposition Equations\n",
    "\n",
    "First of all, let's consider two years 2000 and 2007.\n",
    "\n",
    "We'll find out how each variable contributed to the increase in emissions between 2000 and 2007.\n",
    "\n",
    "Let $t_0$ = 2000 and $t_1$ = 2007.\n",
    "\n",
    "We need to make $\\mathbf{i_0}$, $\\mathbf{i_1}$, $\\mathbf{\\Delta i}$, $\\mathbf{s_0}$, $\\mathbf{s_1}$, $\\mathbf{\\Delta s}$, $\\mathbf{p_0}$, $\\mathbf{p_1}$, $\\mathbf{\\Delta p}$\n",
    "\n",
    "Try\n",
    "\n",
    "```python\n",
    "i_0 = i[2000]\n",
    "i_1 = i[2007]\n",
    "D_i = i_1-i_0\n",
    "\n",
    "s_0 = s[2000]\n",
    "s_1 = s[2007]\n",
    "D_s = s_1-s_0\n",
    "\n",
    "p_0 = p.loc[2000]\n",
    "p_1 = p.loc[2007]\n",
    "D_p = p_1-p_0\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "i_0 = i[2000]\n",
    "i_1 = i[2007]\n",
    "D_i = i_1-i_0\n",
    "\n",
    "s_0 = s[2000]\n",
    "s_1 = s[2007]\n",
    "D_s = s_1-s_0\n",
    "\n",
    "p_0 = p.loc[2000]\n",
    "p_1 = p.loc[2007]\n",
    "D_p = p_1-p_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to make $\\mathbf{i_{effect}}$, $\\mathbf{s_{effect}}$ and $\\mathbf{p_{effect}}$\n",
    "\n",
    "Remember \n",
    "\n",
    "$\\mathbf{i_{effect}} = \\frac{1}{2}(\\mathbf{\\Delta i}\\mathbf{s_0}\\mathbf{p_0}+\\mathbf{\\Delta i}\\mathbf{s_1}\\mathbf{p_1})$\n",
    "\n",
    "$\\mathbf{s_{effect}} = \\frac{1}{2}(\\mathbf{i_0}\\mathbf{\\Delta{s}}\\mathbf{p_1}+\\mathbf{i_1}\\mathbf{\\Delta{s}}\\mathbf{p_0})$\n",
    "\n",
    "$\\mathbf{p_{effect}} = \\frac{1}{2}(\\mathbf{i_1}\\mathbf{s_1}\\mathbf{\\Delta{p}}+\\mathbf{i_0}\\mathbf{s_0}\\mathbf{\\Delta{p}})$\n",
    "\n",
    "Here is the code for ```i_effect``` and ```p_effect```\n",
    "\n",
    "Can you run these and also generate ```s_effect```?\n",
    "\n",
    "```python\n",
    "\n",
    "i_effect = 0.5*((np.dot(D_i,s_0)*p_0.values)+(np.dot(D_i,s_1)*p_1.values))\n",
    "\n",
    "\n",
    "p_effect = 0.5*((np.dot(i_1,s_1)*D_p.values)+(np.dot(i_0,s_0)*D_p.values))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "i_effect = 0.5*((np.dot(D_i,s_0)*p_0.values)+(np.dot(D_i,s_1)*p_1.values))\n",
    "\n",
    "s_effect = 0.5*((np.dot(i_0,D_s)*p_1.values)+(np.dot(i_1,D_s)*p_0.values))\n",
    "\n",
    "p_effect = 0.5*((np.dot(i_1,s_1)*D_p.values)+(np.dot(i_0,s_0)*D_p.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try:\n",
    "\n",
    "```python\n",
    "print('The effect of a change in product intensities is ', int(i_effect[0,0]), ' kilotonnes CO2') \n",
    "\n",
    "print('The effect of a change in spend on products per person  is ', int(s_effect[0,0]), ' kilotonnes CO2') \n",
    "\n",
    "print('The effect of a change population is ', int(p_effect[0,0]), ' kilotonnes CO2')\n",
    "\n",
    "total = i_effect + s_effect + p_effect\n",
    "\n",
    "print('The total change  is ', int(total[0,0]), ' kilotonnes CO2')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The effect of a change in product intensities is  -62442  kilotonnes CO2\n",
      "The effect of a change in spend on products per person  is  133725  kilotonnes CO2\n",
      "The effect of a change population is  26355  kilotonnes CO2\n",
      "The total change  is  97638  kilotonnes CO2\n"
     ]
    }
   ],
   "source": [
    "print('The effect of a change in product intensities is ', int(i_effect[0,0]), ' kilotonnes CO2') \n",
    "\n",
    "print('The effect of a change in spend on products per person  is ', int(s_effect[0,0]), ' kilotonnes CO2') \n",
    "\n",
    "print('The effect of a change population is ', int(p_effect[0,0]), ' kilotonnes CO2')\n",
    "\n",
    "total = i_effect + s_effect + p_effect\n",
    "\n",
    "print('The total change  is ', int(total[0,0]), ' kilotonnes CO2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should have found that the change in product emissions has an effect of reducing the UK footprint by 62,442 Ktonnes CO2. The spend per person increased it by 133,725 Ktonnes and population increase increases it by 26,355 Mtonnes.\n",
    "\n",
    "\n",
    "The overall effect is an increase of 97,638 Ktonnes CO2 from 2000. \n",
    "\n",
    "This should match the value in your table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2.3 What about the recession?\n",
    "\n",
    "Can you repeat what you did in the previous exercise but now  $t_0$=2007 and $t_1$ = 2014?\n",
    "\n",
    "This will look at how each variable contributed to the decrease in emissions between 2007 and 2014.\n",
    "\n",
    "Start by making $\\mathbf{i_0}$, $\\mathbf{i_1}$, $\\mathbf{\\Delta i}$, $\\mathbf{s_0}$, $\\mathbf{s_1}$, $\\mathbf{\\Delta s}$, $\\mathbf{p_0}$, $\\mathbf{p_1}$, $\\mathbf{\\Delta p}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "i_0 = i[2007]\n",
    "i_1 = i[2014]\n",
    "D_i = i_1-i_0\n",
    "\n",
    "s_0 = s[2007]\n",
    "s_1 = s[2014]\n",
    "D_s = s_1-s_0\n",
    "\n",
    "p_0 = p.loc[2007]\n",
    "p_1 = p.loc[2014]\n",
    "D_p = p_1-p_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to make $\\mathbf{i_{effect}}$, $\\mathbf{s_{effect}}$ and $\\mathbf{p_{effect}}$ and display the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The effect of a change in product intensities is  -86942  kilotonnes CO2\n",
      "The effect of a change in spend on products per person  is  -78388  kilotonnes CO2\n",
      "The effect of a change population is  33227  kilotonnes CO2\n",
      "The total change  is  -132104  kilotonnes CO2\n"
     ]
    }
   ],
   "source": [
    "i_effect = 0.5*((np.dot(D_i,s_0)*p_0.values)+(np.dot(D_i,s_1)*p_1.values))\n",
    "\n",
    "s_effect = 0.5*((np.dot(i_0,D_s)*p_1.values)+(np.dot(i_1,D_s)*p_0.values))\n",
    "\n",
    "p_effect = 0.5*((np.dot(i_1,s_1)*D_p.values)+(np.dot(i_0,s_0)*D_p.values))\n",
    "\n",
    "print('The effect of a change in product intensities is ', int(i_effect[0,0]), ' kilotonnes CO2') \n",
    "\n",
    "print('The effect of a change in spend on products per person  is ', int(s_effect[0,0]), ' kilotonnes CO2') \n",
    "\n",
    "print('The effect of a change population is ', int(p_effect[0,0]), ' kilotonnes CO2')\n",
    "\n",
    "total = i_effect + s_effect + p_effect\n",
    "\n",
    "print('The total change  is ', int(total[0,0]), ' kilotonnes CO2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should have found that the change in product emissions has an effect of decreasing the UK footprint by 86,942 Ktonnes CO2. The spend per person decreased it by 78,388 Ktonnes and population increase increases it by 33,227 Mtonnes.\n",
    "\n",
    "The overall effect is a decrease of 132,104 Ktonnes CO2 between 2007 and 2014. \n",
    "\n",
    "This should match the value in your table when you subtract emissions in 2014 from emissions in 2007.\n",
    "\n",
    "Try\n",
    "\n",
    "```python\n",
    "UK_foot2.loc[2014,'footprint']-UK_foot2.loc[2007,'footprint']\n",
    "```\n",
    "\n",
    "to check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-132104.15484544775"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UK_foot2.loc[2014,'footprint']-UK_foot2.loc[2007,'footprint']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3.1 Putting it all together in a for loop\n",
    "\n",
    "We are now going to put all of this together in a for loop which calculates the change from 1995 for each year.\n",
    "\n",
    "\n",
    "Try:\n",
    "\n",
    "```python\n",
    "sda_data = np.zeros((15,4))\n",
    "\n",
    "for count, yr in enumerate(years):\n",
    "    i_0 = i[2000]\n",
    "    i_1 = i[yr]\n",
    "    delta_i = i_1-i_0\n",
    "    s_0 = s[2000]\n",
    "    s_1 = s[yr]\n",
    "    delta_s = s_1-s_0\n",
    "    p_0 = p.loc[2000]\n",
    "    p_1 = p.loc[yr]\n",
    "    delta_p = p_1-p_0\n",
    "    \n",
    "    i_effect = 0.5*((np.dot(delta_i,s_0)*p_0.values)+(np.dot(delta_i,s_1)*p_1.values))\n",
    "    s_effect = 0.5*((np.dot(i_0,delta_s)*p_1.values)+(np.dot(i_1,delta_s)*p_0.values))\n",
    "    p_effect = 0.5*((np.dot(i_1,s_1)*delta_p.values)+(np.dot(i_0,s_0)*delta_p.values))\n",
    "    \n",
    "    sda_data[count,0] = i_effect[0,0]\n",
    "    sda_data[count,1] = s_effect[0,0]\n",
    "    sda_data[count,2] = p_effect[0,0]\n",
    "    sda_data[count,3] = sda_data[count,0]+sda_data[count,1]+sda_data[count,2]\n",
    "    \n",
    "UK_SDA = pd.DataFrame(sda_data,index=years,columns=['Product emissions','Spend per capita','Population','Total change'])\n",
    "UK_SDA\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product emissions</th>\n",
       "      <th>Spend per capita</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2323.98</td>\n",
       "      <td>13418.08</td>\n",
       "      <td>2358.68</td>\n",
       "      <td>18100.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>61.11</td>\n",
       "      <td>25481.89</td>\n",
       "      <td>4995.74</td>\n",
       "      <td>30538.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1611.08</td>\n",
       "      <td>49196.18</td>\n",
       "      <td>8067.83</td>\n",
       "      <td>58875.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>-12947.85</td>\n",
       "      <td>81187.05</td>\n",
       "      <td>11979.33</td>\n",
       "      <td>80218.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>-39944.50</td>\n",
       "      <td>113114.88</td>\n",
       "      <td>16392.90</td>\n",
       "      <td>89563.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>-38073.22</td>\n",
       "      <td>115469.13</td>\n",
       "      <td>21305.69</td>\n",
       "      <td>98701.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>-62442.22</td>\n",
       "      <td>133725.38</td>\n",
       "      <td>26355.20</td>\n",
       "      <td>97638.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>-82147.58</td>\n",
       "      <td>139686.28</td>\n",
       "      <td>31278.79</td>\n",
       "      <td>88817.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>-60360.39</td>\n",
       "      <td>26452.08</td>\n",
       "      <td>33753.61</td>\n",
       "      <td>-154.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>-82425.99</td>\n",
       "      <td>44545.70</td>\n",
       "      <td>38520.46</td>\n",
       "      <td>640.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>-100471.64</td>\n",
       "      <td>40818.27</td>\n",
       "      <td>42651.05</td>\n",
       "      <td>-17002.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>-61274.17</td>\n",
       "      <td>45694.99</td>\n",
       "      <td>48695.78</td>\n",
       "      <td>33116.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>-113694.72</td>\n",
       "      <td>63180.81</td>\n",
       "      <td>51547.21</td>\n",
       "      <td>1033.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>-137242.08</td>\n",
       "      <td>48327.95</td>\n",
       "      <td>54448.33</td>\n",
       "      <td>-34465.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Product emissions  Spend per capita  Population  Total change\n",
       "2000               0.00              0.00        0.00          0.00\n",
       "2001            2323.98          13418.08     2358.68      18100.74\n",
       "2002              61.11          25481.89     4995.74      30538.74\n",
       "2003            1611.08          49196.18     8067.83      58875.09\n",
       "2004          -12947.85          81187.05    11979.33      80218.54\n",
       "2005          -39944.50         113114.88    16392.90      89563.28\n",
       "2006          -38073.22         115469.13    21305.69      98701.59\n",
       "2007          -62442.22         133725.38    26355.20      97638.36\n",
       "2008          -82147.58         139686.28    31278.79      88817.49\n",
       "2009          -60360.39          26452.08    33753.61       -154.70\n",
       "2010          -82425.99          44545.70    38520.46        640.17\n",
       "2011         -100471.64          40818.27    42651.05     -17002.31\n",
       "2012          -61274.17          45694.99    48695.78      33116.60\n",
       "2013         -113694.72          63180.81    51547.21       1033.30\n",
       "2014         -137242.08          48327.95    54448.33     -34465.79"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sda_data = np.zeros((15,4))\n",
    "\n",
    "for count, yr in enumerate(years):\n",
    "    i_0 = i[2000]\n",
    "    i_1 = i[yr]\n",
    "    delta_i = i_1-i_0\n",
    "    s_0 = s[2000]\n",
    "    s_1 = s[yr]\n",
    "    delta_s = s_1-s_0\n",
    "    p_0 = p.loc[2000]\n",
    "    p_1 = p.loc[yr]\n",
    "    delta_p = p_1-p_0\n",
    "\n",
    "    i_effect = 0.5*((np.dot(delta_i,s_0)*p_0.values)+(np.dot(delta_i,s_1)*p_1.values))\n",
    "    s_effect = 0.5*((np.dot(i_0,delta_s)*p_1.values)+(np.dot(i_1,delta_s)*p_0.values))\n",
    "    p_effect = 0.5*((np.dot(i_1,s_1)*delta_p.values)+(np.dot(i_0,s_0)*delta_p.values))\n",
    "\n",
    "    sda_data[count,0] = i_effect[0,0]\n",
    "    sda_data[count,1] = s_effect[0,0]\n",
    "    sda_data[count,2] = p_effect[0,0]\n",
    "    sda_data[count,3] = sda_data[count,0]+sda_data[count,1]+sda_data[count,2]\n",
    "\n",
    "UK_SDA = pd.DataFrame(sda_data,index=years,columns=['Product emissions','Spend per capita','Population','Total change'])\n",
    "UK_SDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you make the following Chart?\n",
    "\n",
    "<img width=\"483\" alt=\"Screenshot 2022-01-28 at 14 35 01\" src=\"https://github.com/earao/images/blob/main/Screenshot%202023-12-22%20at%2015.53.07.png?raw=true\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'change from 2000 (kilotonnes CO2)')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = UK_SDA.plot( kind = 'line')\n",
    "chart.set_ylabel('change from 2000 (kilotonnes CO2)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Exercise 4.1 Can you try this for a different country?\n",
    "\n",
    "You'll need to find population data for your country of choice.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Exercise 4.2 Can you try alternative decompositions?\n",
    "\n",
    "You might want to try:\n",
    "\n",
    "$$Q = eLsp$$\n",
    "\n",
    "or what happens if you try and split spend per product per person into two further variables where $s$ is the product of basket of goods multiplied by total per capita spend\n",
    "\n",
    "This is starting to get really tricky. You might find the matrix multiplication causes headaches. This is why we always set up a checking table first.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Key learning points\n",
    "You should have learnt:\n",
    "<ol>\n",
    "<li>How to rewrite the Leontief equation into something more like IPAT/Kaya</li>\n",
    "<li>How to form the variables that can be used in a Structural Decomposition Analysis</li>\n",
    "<li>How to calculate SDA</li>\n",
    "<li>How interpret the results of a SDA</li>\n",
    "   \n",
    "</ol>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
